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Assumptions Behind the Linear Regression Model
Pfeifer, Phillip E. Case QA-0271 / Published April 5, 1983 / 10 pages.
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Product Overview

In a previous note, "Introduction to Least-Squares Modeling" (UVA-QA-0500), we have seen how least squares can be used to fit the simple linear model to historical data. The resulting model can then be used to forecast the next occurrence of Y, the dependent variable, for a given value of X, the independent variable. This use of least squares to fit a forecasting model requires no assumptions. It can be applied to almost any situation, and a reasonable forecast results. At this level of analysis, least-squares modeling is equivalent simply to fitting a straight line through a cloud of points and interpolating or extrapolating for a new value of Y for a given X using the fitted line.

  • Overview

    In a previous note, "Introduction to Least-Squares Modeling" (UVA-QA-0500), we have seen how least squares can be used to fit the simple linear model to historical data. The resulting model can then be used to forecast the next occurrence of Y, the dependent variable, for a given value of X, the independent variable. This use of least squares to fit a forecasting model requires no assumptions. It can be applied to almost any situation, and a reasonable forecast results. At this level of analysis, least-squares modeling is equivalent simply to fitting a straight line through a cloud of points and interpolating or extrapolating for a new value of Y for a given X using the fitted line.

  • Learning Objectives